Multiple-Input Multiple-Output (MIMO) systems utilize multiple antennas at the transmitter and receiver on both sides of a wireless communication channel. This can increase data rates without requiring additional power, by permitting concurrent transmittal of several streams of data, and by performing spatial information processing. MIMO schemes enable the transmitter to transform a data stream to multiple streams and send them through multiple transmit antennas. A multiple antenna receiver processes the received streams and reconstructs the original information. In an ideal network, MIMO capacity increases linearly with the number of antennas at the receiver or at the transmitter, whichever is lower.
In a multi antenna setting, the transmitted signal is linearly transformed by the channel matrix, which is a consequence of the physical environment. The essential goal of MIMO techniques is minimization of the streams' mutual interference. This is done by introducing a suitable linear matrix to process the transmitted and received signals such that the streams become close to or entirely separated. Ideally, accurate Channel State Information (CSI), i.e. the channel matrix, is provided to both transmitter and receiver. In reality, only channel estimation or approximation is available, which is established and maintained by sending pilot signals.
MIMO processing may be performed as either an open loop or a closed loop technique. In open loop MIMO, the transmitter has no specific knowledge of the condition of the channel before data signals are transmitted to the receiver. Common open loop receiver schemes include Minimum Mean Squared Error (MMSE) filtering, QR decomposition, Minimum Likelihood (ML) and Zero Forcing (ZF).
In closed loop MIMO, the transmitter can use CSI to pre-process input signals prior to their transmission, where this CSI is obtained and updated through pilot feedback or explicit channel information sent by the receiver to the transmitter. In this manner, performance is improved and the receiver's processing may be simplified. A well-known closed loop method is Singular Value Decomposition (SVD).
A brief survey of the mathematical and algorithmic tools utilized by this invention is as follows. A triangular matrix is a matrix whose non zero entries are all above or all below the main diagonal. In a unitary matrix, the columns are mutually orthogonal unit vectors. QR is the expression of a matrix as a product of a triangular matrix and a unitary matrix. SVD is the expression of a matrix as a product of a diagonal matrix (i.e., triangular matrix whose non zero entries are all on the main diagonal) with two unitary matrices—one on each side. Bi-diagonalization is the expression of a matrix as a product of a bi-diagonal matrix (i.e., triangular matrix whose non zero entries are all on the main diagonal and in one of the second-main diagonals) with two unitary matrices, one on each side. Any matrix can be decomposed in each of these manners.
The literature provides numerous efficient algorithms that compute SVD, QR, and bi-diagonalization with various effective tools that include Householder matrix and Jacobi rotations. Particularly effective algorithms can be found in the book, Gene H. Golub and Charles F. Van Loan: “Matrix Computations” The John Hopkins University Press, Baltimore, Md., Third edition 1996, (hereinafter: [Matrix]) and the lecture notes of Gene H. Golub: (Stanford University, Stanford, USA), “Lectures on Matrix Computations 2004” Apr. 15-Jun. 10, 2004. http://www.mat.uniroma1.it/%7Ebertaccini/seminars/CS339/syllabus.html (hereinafter: [Golub]), and the numerous references mentioned therein.
In the MIMO communication context, when the channel matrix is decomposed into a product of a few product-matrices, a unitary product matrix can be neutralized by linear processing done respectively at the transmitter or the receiver. Linear unitary processing at the receiver and/or the transmitter has the unique desired property that it can decrease the mutual interference of MIMO streams, without affecting transmission energy, and without amplifying the additive noise term. This is due to the special characteristic of a unitary matrix, that it is Euclidean norm invariant (“∥U·x∥≡∥x∥ for all vector x when U is unitary”). The effective channel matrix is a product of all linear processing with the natural wireless channel. The diagonal elements of the effective channel matrix represent the channel amplification of the respective data streams, while the non diagonal elements represent the strength of mutual interference.
It has been shown that SVD provides the optimal solution for MIMO communication by eliminating all interference (represented by the non-diagonal entries of the resulting channel matrix). However, its formidable complexity and high sensitivity to channel fluctuations and estimation errors make it impractical for rapidly varying channels and call for alternative solutions. A good measure of the strength of the diagonal of a processed channel matrix, i.e., the relative strength of the desired signals compared to their mutual interference, is the Frobenius norm that sums the squares of a.v. (absolute values) of the respective matrix-entries. It is notable that the Frobenius norm of a matrix is invariant under products with unitary matrix, that is, for any matrices H & U such that U is unitary, and H·U is defined, the sum of a.v. of squares of all H entries is equal to sum of a.v. of squares of all H·U entries.
The present invention evolved from two central papers on MIMO wireless transmission. The paper of Emre Telatar: “Capacity of multi-antenna Gaussian channels”, 1995 (hereinafter: [Telatar]) showed that when receiver and transmitter both have perfect CSI, an SVD-based scheme achieves maximal throughput and full interference cancellation. The paper of Martin Schubert and Holger Boche, [“Throughput Maximization for Uplink and Downlink Beamforming with Independent Coding”, 2003 Conference on Information Sciences and Systems, The Johns Hopkins University, Mar. 12-14, 2003], hereinafter: [Boche], showed—with other works of the same authors—that when only one side (transmitter or receiver) has CSI, MMSE provides the optimal linear beamforming solution.
Another significant relevant work is that of Guillaume Lebrun et al, [Guillaume Lebrun, Jason Gao, and Michael Faulkner, “MIMO Transmission Over a Time-Varying Channel Using SVD” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 4, NO. 2, MARCH 2005] (hereinafter: [Time-Varying . . . SVD]) which describes a scheme of SVD-based transmitter and MMSE receiver and points to the main advantages of such a combined scheme: optimal throughput when full CSI is provided and effective mitigation of performance degradation when the transmitter CSI is outdated and/or not accurate. This scheme would be called here SVD-MMSE. Their approach is particularly suitable when the communication channel varies over time and frequency and, due to channel estimation limitations, 2 sided SVD shows degradation in performance.
The recently published patent application: [US 2006/0234645 of Lin et al (19 Oct. 2006)] (hereinafter: [LC]) considers also an SVD-MMSE scheme. It realizes at section [0018] that the open loop transmission standard, IEEE 802.11n, where the transmitter has no CSI, makes MMSE linear filtering a desirable receiver scheme. This patent suggests that the already existing MMSE circuitry can be utilized also for a closed loop transmission scheme, which includes beam-forming by the transmitter. This approach is particularly useful for a mobile station with computing power limited to MMSE filtering. Thus, section [0017] points to the fact that their method enables the benefits of SVD performance when only one side has the expensive circuitry required for SVD. This patent treats the realistic situation where, even when CSI exists, it might be outdated and/or inaccurate due to channel estimation corruption. [LC] suggests a solution to this situation suitable for slowly varying TDD (time-division duplex) channels. Section [0020] of [LC] describes an interesting and useful scheme in which a wireless radio (e.g. mobile), incapable of computing its own SVD, can use the received signals to conclude the required SVD processing matrix for its transmit signals.
To summarize the motivation of the present invention, it will be appreciated by those skilled in the art that SVD is an expensive application whose circuitry and utilization should be eliminated or minimized. Therefore, it would be desirable to have an effective closed loop MIMO system providing linear processing at the transmitter and at the receiver that does not require SVD circuitry, or utilizes a diluted form of SVD.